The following paragraph will present the method at work to compare the aggressiveness of the different combinations of vehicles.
Data regarding the load repartition of current A40s is not available. Nevertheless, a range of aggressive-ness for current A40s can be specified: between the reference (1) and the maximum (or present worst) value calculated for A40. Two horizontal segments surround this area on the graphics below. It has then be decided to use the calculated median value (in yellow) for each combination to range them from least to most aggressive, showing at the same time the extreme values. Each kind of pavement has been con-sidered separately. Combinations shown on the left of A40 on the graph below are less aggressive than the actual full loaded 5 axles 40 t. The most aggressive combination is shown on the extreme right side of the graph. The shape order with respect to aggressiveness would change if one decides to use the best or worst values as classification criterion.
Please note that the scales of the graphs are different.
Figure 27: Aggressiveness of each vehicle combination toward flexible pavements, supporting a low traffic
Flexible pavement low traffic
0 0,2 0,4 0,6 0,8 1 1,2 1,4 1,6 1,8 2
G50 F50 C40 E50 D46 A40 C44 B40 G60 C48 F60 B44 A44 E60
Worst Median Best Present worst Reference
Figure 28: Aggressiveness of each vehicle combination toward bituminous pavements, supporting a moderate traffic Bitum inous pavement
m oderate traffic
0 0,4 0,8 1,2 1,6 2
G50 F50 E50 C40 D46 C44 A40 G60 C48 B44 E60 A44 B40 F60
Worst Median Best Present w orst Reference
Figure 29: Aggressiveness of each vehicle combination toward bituminous pavements, supporting an heavy traffic
G50 F50 E50 C40 D46 C44 A40 G60 B40 C48 B44 E60 F60 A44
Worst Median Best Present w orst Reference
Figure 30: Aggressiveness of each vehicle combination toward semi-flexible pavements, supporting a heavy traffic Semi-flexible pavement
G50 F50 E50 B40 D46 C40 A40 G60 C44 C48 B44 E60 F60 A44
Worst
• Semi-flexible pavements with heavy traffic are the most sensitive to axles' load (with the highest calcu-lated aggressiveness) and to load repartition (aggressiveness amplitude between best and worst cases).
• G50 and F50 are far better than the current situation, for all kind of pavements. They are followed by E50, C40, and D46.
• B40, which complies with current directive, is sometimes better and sometimes worse than A40, our reference.
• Aggressiveness of combination C44 is very close to the one of the reference truck (twice less, twice more).
• G60 seems as acceptable as C48, but it is probably because of the low value of its relative aggressive-ness, when ideally loaded.
• For the two cases related to heavy traffic (thick bituminous and semi flexible), we find the same order:
B44, E60, F60 and A44 (semi trailer, 44 t, five axles, which is also the second worst for the other two cases).
• Ranking is different for the low traffic and moderate traffic cases.
2.6. Indicators
It seems feasible to define a global aggressiveness indicator once we know the composition of the network where the LHVs are allowed.
Attention must be paid to the fact that using the median value does not enable to observe the large varia-tion in aggressiveness values that depends upon the way vehicles are loaded (for example G60 versus C48). Some research should be done to have a more accurate knowledge on the existing load repartition.
To compute the relevant indicators, a representative network is modelled, that is made up of 5 % of low traffic – flexible pavement, 15 % of moderate traffic – bituminous pavement and 40 % for each other kind of roads. Then, the aggressiveness due to the traffic of different kinds of combinations is calculated;
depending on the manner vehicles are loaded. The three values given for each vehicle combination corre-spond to the load scenarios, that is to say: best-loaded, worse-loaded and a median load.
Figure 31: Aggressiveness of each vehicle combination toward a “representative” modelled pavement
Indicators
0 0,5 1 1,5 2 2,5 3
G50 F50 E50 C40 D46 B40 A40 C44 G60 C48 B44 E60 F60 A44
Worst Median Best
It can be observed that:
• LHVs with a weight limit of 50 t are better for pavements than the reference (present semi-trailer 5 axles 40 t), their aggressiveness being approximately the half of the one of the reference.
• A semi-trailer 5 axles 44 t is the most aggressive vehicle (some 2.4 more aggressive than the refer-ence).
• Two LHVs with a weight limit of 60 t are twice as aggressive as the reference while the third (shape G: three axles tractor, a little semi-trailer plus a big semi-trailer) is "only" 1.4 more aggressive than the reference, thus in the same range of the median value for A40 between the extreme load cases.
• In the case of G60, the ideal way of loading leads to an aggressiveness that is 30% lower than the ref-erence one: it is very important to explain which is the ideal way of loading, even if it depends on the
Ideal loads
For the considered network (5% of low traffic – flexible pavement, 15% of moderate traffic - bituminous pavement and 40 % for each other kind of roads), and using an approximation to the nearest half tonne, it is interesting to calculate the axle load that would overall minimise the aggressiveness on pavements (knowing that the situation will be suboptimal for any kind of pavement in particular).
Table 45: Axle loads minimising the aggressiveness of each vehicle combination on a “representative” modelled pave-ment
Code e 1 e 2 e 3 e 4
A40 8 11 21 - A44 8.5 11.5 24 - B44 8 18 18 - C40 7.5 14.5 18 -
C44 8 16.5 19.5 -
C48 8 18 22 - D46 6 11.5 16 12.5
E50 7 12.5 16.5 14
F50 6 13 14 17
G50 6 12.5 15.5 16
E60 8 16.5 19.5 16
F60 8 17.5 14 20.5 G60 8 16 18 18
Another important indicator could be the relative aggressiveness per tonne carried. With the same net-work, the aggressiveness per tonne of goods carried is shown on the graphic below.
Figure 32: Aggressiveness of each vehicle combination toward a “representative” modelled pavement, related to a tonne of transported goods
Indicators per ton
0,0 0,5 1,0 1,5 2,0 2,5 3,0
G50 F50 E50 C40 G60 D46 C44 B40 F60 E60 A40 C48 B44 A44
Worst median best
present worst reference
There are only three shapes worse than the reference one: C48, B44 and, the worst, A44.
Financial overall idea
French experts calculated in 2003 what would be the extra cost of maintenance if A44 or C44 were al-lowed. The calculations only considered three levels of traffic, without considering the actual structure, for the French national road network.
Table 46: Calculation of extra maintenance costs for France in 2003
Number of HGV (heavy goods vehicle)per day and per direction A44 C44
750 to 2000 14% 10%
300 to 750 17% 12%
150 to 300 20% 15%
Once again, we reach to the conclusion that A44 and C44 would generate extra road maintenance costs, A44 being worse than C44 and should therefore be avoided.
3. Conclusions on infrastructure
Summarising the whole chapter, and indicating in the pavement column the median value of the indicator calculated previously (relative aggressiveness on a network made up of 5 % of low traffic – flexible ment, 15 % of moderate traffic - bituminous pavement, 40 % of heavy traffic – thick bituminous pave-ment and 40 % of heavy traffic – semi-flexible pavepave-ment), the main results are shown in the simplified table below.
Figure 33: Summary of the consequences on infrastructures, without countermeasures
No consequences Moderate consequences Important consequences
Bridges
Code Shape Pavement Extreme
loads Fatigue
A44 2.39
A48 >2.39
B40 1.22
B44 1.92
B48 >1.92
C40 1.02
C44 1.42
C48 1.85
Bridges
Code Shape Pavement Extreme
loads Fatigue
D46 1.04
E50 0.55
F50 0.53
G50 0.42
E60 2.05
F60 2.07
G60 1.46
This table gives an overview of the impacts that result from the traffic of different combinations of vehi-cles, with different GVW (gross vehicle weight), driving on different kinds of pavements. Using a basic colour code, it allows a rough comparison of all cases. It clearly shows that, in some cases (in red), important consequences have to be expected and that the corresponding combinations (A44, A48, B44, B48, C48, E60, F60 and G60) should be avoided. The 44 tonnes on 5 axles (A44 combination, 2 axle tractor and 3 axle tridem semi-trailer) is very bad for the infrastructures, bridges and pavements. If the Directive is modified in the future, this configuration should best be avoided in all EU State Members, even those which already authorized this configuration (e.g. France, Belgium, Italy).
It must be reminded that appropriate countermeasures could help to decrease the impact on bridges, and hence change the result presented in the table above. Among these countermeasures could be mentioned:
• Training the industry about the best way to load a lorry.
• Minimal spacing between two LHVs.
• No overtaking.
• On-board load measuring systems.
• Authorisations limited to specific routes.
It is therefore essential to define the relevant itineraries, to identify the problematic bridges and to decide of the appropriate measures that should be implemented. However, these three tasks require time and exhaustive expertise. Some possible countermeasures will be discussed later in this report, along with pro-posals for further studies.
VII Effect on energy efficiency, CO 2 and noxious emissions
1. Description of emissions
Energy efficiency of freight transport is measured in terms of energy consumption per tonne-km. For road transport, this is generally equivalent to fuel consumption, more specifically diesel fuel. As such, im-proving energy efficiency is closely linked to decreasing operational costs.
For rail the picture is somewhat more complex. Some 20 % of freight trains are diesel powered. The pro-pulsion force of the other 80 % is electricity. In order to account for the total emissions generated by freight transport, the complete energetic cycle needs to be examined, from well to wheels. Power plants in European countries tend to vary: electricity produced in France will generate few emissions, as close to 80% originates from nuclear plants. About 55 % of Austrian electricity comes from hydropower; none-theless, fossil fuel plants are still a major source of power in many European countries.
CO2 emissions are directly related to fuel consumption. For each litre of diesel fuel that is consumed, 2.62 kg of CO2 is emitted into the air.55
NOx is a generic term for mono-nitrogen oxides (NO and NO2). Ground-level (tropospheric) ozone (smog) is formed when NOx and volatile organic compounds (VOCs) react in the presence of sunlight.
Children, people with lung diseases such as asthma, and people who work or exercise outside are suscep-tible to adverse effects such as damage to lung tissue and reduction in lung function. Ozone can be trans-ported by wind currents and cause health impacts far from original sources. Other impacts from ozone include damaged vegetation and reduced crop yields.
PM or particulate matter are tiny particles of solid or liquid suspended in a gas. It is generally classified based on its diameter, ranging from 10 µm to smaller than 0.1µm. The external costs of PM are due to its impact on human (and animal) health. Inhalation of the bigger particles (between 2.5 µm and 10 µm) can cause pulmonary diseases such as asthma or lung cancer. Emissions of traffic are mainly PM below 2.5 µm. Inhaling particles of that size can also lead to cardiovascular problems. The road transport sector contributes with both vehicle exhaust particles and resuspension of road dust.
2. Methodology
The COPERT IV methodology56 has been used to calculate fuel consumption and CO2 emissions.
COPERT is a software program aiming at the calculation of air pollutant emissions from road transport.
The development of COPERT has been financed by the EEA. COPERT IV estimates emissions of all
major air pollutants (CO, NOx, VOC, PM, NH3, SO2, heavy metals) produced by different vehicle catego-ries (passenger cars, light duty vehicles, heavy duty vehicles, mopeds and motorcycles) as well as green-house gas emissions (CO2, N2O, CH4). In this study, the COPERT formulas for LHV were used for PM, NOx, and CO2.
The composition of the truck fleet (age classes, Euro classes) was derived from the TREMOVE model57. TREMOVE is a policy assessment model to study the effects of different transport and environment policies on the emissions of the transport sector. The model estimates the transport demand, modal shifts, vehicle stock renewal and scrappage decisions as well as the emissions of air pollutants and the welfare level, for policies as road pricing, public transport pricing, emission standards, subsidies for cleaner cars etc. The model covers passenger and freight transport in 31 countries and covers the period 1995-2030.
The output of the scenario calculations are tonnes transported, vehicle kilometres and tonne kilometres, disaggregated based on
• truck type,
• truck technology,
• region (urban/motorway/rural road),
• timing (peak/off peak),
• load factor.
For each class, data from the demand calculations served as the input for the calculation.
Trucks are distinguished in TREMOVE based on their GVW (gross vehicle weight). In the standard model, four types exist:
• 3.5 t - 7.5 t (HDT1)
• 7.5 t - 16 t (HDT2)
• 16 t - 32 t (HDT3)
• 32 t - 40 t (HDT4)
While this is sufficient for the base case, the other scenarios require modelling greater gross vehicle weights. For that, two types are added:
• 40 t - 50 t (HDT5)
• 50 t - 60 t (HDT6)
COPERT IV works with a different set of truck types. These are:
• Rigid
- 34 t - 40 t (HDT_ARTIC4)
- 40 t - 50 t (HDT_ARTIC5)
- 50 t - 60 t (HDT_ARTIC6)
A link exists between these classifications. The column “proportion” shows the share of the COPERT type in the TREMOVE type:
Table 47: TREMOVE-COPERT link for vehicle types
TREMOVE TREMOVE description COPERT COPERT description value HTD1 heavy duty truck 3.5-7.5t - diesel HDT_RIGID1 RT <=7.5t 1 HTD2 heavy duty truck 7.5-16t - diesel HDT_RIGID8 RT >7.5-12t 0.25 HTD2 heavy duty truck 7.5-16t - diesel HDT_RIGID2 RT >12-14t 0.25 HTD2 heavy duty truck 7.5-16t - diesel HDT_RIGID3 RT >14-20t 0.25 HTD2 heavy duty truck 7.5-16t - diesel HDT_ARTIC1 TT/AT >14-20t 0.25 HTD3 heavy duty truck 16-32t - diesel HDT_ARTIC1 TT/AT >14-20t 0.1 HTD3 heavy duty truck 16-32t - diesel HDT_ARTIC2 TT/AT >20-28t 0.16 HTD3 heavy duty truck 16-32t - diesel HDT_ARTIC3 TT/AT >28-34t 0.16 HTD3 heavy duty truck 16-32t - diesel HDT_RIGID3 RT >14-20t 0.1 HTD3 heavy duty truck 16-32t - diesel HDT_RIGID4 RT >20-26t 0.16 HTD3 heavy duty truck 16-32t - diesel HDT_RIGID5 RT >26-28t 0.16 HTD3 heavy duty truck 16-32t - diesel HDT_RIGID6 RT >28-32t 0.16 HTD4 heavy duty truck >32t - diesel HDT_ARTIC4 TT/AT >34-40t 0.25 HTD4 heavy duty truck >32t - diesel HDT_ARTIC5 TT/AT >40-50t 0.25 HTD4 heavy duty truck >32t - diesel HDT_ARTIC6 TT/AT >50-60t 0.25 HTD4 heavy duty truck >32t - diesel HDT_RIGID7 RT >32t 0.25
In this study it is assumed the intramodal shift to LHV only comes from HDT4. The COPERT IV methodology allows establishing functions that will link speed with fuel consumption for all classes. To achieve a flexible automated calculation tool, the COPERT IV functions that are in TREMOVE are pro-grammed into an Access database.
A major parameter in determining exhaust emissions is the load factor of trucks. It is calculated as the av-erage load of a truck, divided by its maximal capacity. The avav-erage load is based on the scenario output, as [number of tonne-km]/[number of vehicle-km]. The average maximum capacity is displayed in Table 48.
Table 48: Load capacities per truck type Truck type Load capacity (tonne)
HDT1 3.5
HDT2 8.5
HDT3 14
HDT4 26
HDT5 29
HDT6 39.5
Five formulas are established to calculate fuel consumption. Fourteen formulas are used to calculate NOx, while nine are used for PM, depending on the emission profile by each subclassification. They vary be-tween truck types, truck technologies and load factors. The parameters of the formulas are vehicle speed, plus a number of COPERT specific data. For details, we refer to the TREMOVE58 and COPERT IV59 websites.
3. Calculation
Using the methodology described above, detailed calculations were made for each scenario. The full well-to-wheels cycle is considered, to allow for comparability between modes. Data are presented in tabular form, grouped by country and truck type. Highly detailed numbers are presented. Of course, these are subject to the same caution that was given in the previous chapters, and depend very much on demand data as described in chapter IV.
3.1. “Business as usual” scenario
In the reference scenario, with only Finland and Sweden using 25.25 m/60 t LHVs, a total of 40 729.26 million litres of diesel fuel is consumed during transport using heavy trucks. The average fuel consump-tion of HDT4 is close to 30.28 l/100 km. Fuel efficiency in terms of consumpconsump-tion (litre) per tonne-km is equal to 0.02567 l/tonne-km . This is equivalent to 67.2554 g of CO2 per tonne-km.
Table 49: Scenario 1 transport energy consumption
Country Truck type Fuel consumption (tonne) Fuel consumption (million litre) CO2 (tonne)
AT HDT4 489 420 586 1 535 601
BE HDT4 1 438 054 1 722 4 512 025
BG HDT4 362 792 434 1 138 292
CZ HDT4 968 494 1 160 3 038 739
DE HDT4 6 896 051 8 259 21 636 994
DK HDT4 294 407 353 923 728
EE HDT4 118 262 142 371 057
ES HDT4 5 852 937 7 010 18 364 127
FI HDT4 105 477 128 330 942
FR HDT4 5 069 512 6 071 15 906 059
GR HDT4 489 361 586 1 535 416
HU HDT4 441 853 529 1386 353
IE HDT4 208 243 249 653 382
IT HDT4 2 983 493 3 573 9 360 984
LT HDT4 221 477 265 694 904
LU HDT4 43 648 52 136 949
LV HDT4 135 925 163 426 477
NL HDT4 855 024 1 024 2 682 715
PL HDT4 2 035 487 2 438 6 386 528
PT HDT4 259 182 310 813 207
RO HDT4 1 136 225 1 361 3 565 012
SE HDT4 154 700 185 485 385
SI HDT4 124 628 149 391 032
SK HDT4 223 434 268 701 044
UK HDT4 2 438 284 2 920 7 650 339
FI HDT6 266 952 324 837 587
SE HDT6 391 094 468 1 227 092
TOTAL 34 004 414 40 729 106 691 971
59 http://lat.eng.auth.gr/copert/
During the production process of the fuel, energy is consumed as well. The CO2 emitted during this pro-duction process (well-to-tank emissions) should also be taken into account. This adds another 19.4 % to the total.
Table 50: Scenario 1 well-to-tank CO2 emissions Country Truck type CO2 well-to-tank emissions (tonne)
AT HDT4 298 546
In the base case, NOx emissions are 483 062 tonne. About 11 511 tonnes of particulate matter are ex-hausted, of which 44 % does not originate from burning fuel, but from other sources such as resuspended dust and mechanical abrasion (tyre, brake and road surface wear).
Table 51: Scenario 1 Noxious emissions
Country Truck type NOx exhaust emissions (tonne) PM exhaust emissions (tonne) PM non-exhaust emissions (tonne)
AT HDT4 6 818.381 90.806 69.520
Country Truck type NOx exhaust emissions (tonne) PM exhaust emissions (tonne) PM non-exhaust emissions (tonne)
Noxious emissions from the fuel production process are clearly following a different pattern than the emissions from transport. Well-to-tank PM emissions are nearly at the same level as emissions from fuel consumption, whereas NOx emitted in production is only 1/8 of the total nitrous oxide emitted in the fuel life cycle.
Table 52: Scenario 1 Well-to-tank noxious emissions
Country Truck type NOx well-to-tank (tonne) PM well-to-tank (tonne)
AT HDT4 994.884 153.722
Country Truck type NOx well-to-tank (tonne) PM well-to-tank (tonne)
UK HDT4 4 956.494 765.841
FI HDT6 542.655 83.847
SE HDT6 795.007 122.839
TOTAL 69 123.494 10 680.447
3.2. “Full option” scenario
The full option scenario allows LHVs of 25.25 m and 60 t to circulate throughout the European Union. In spite of a volume (tonne-km) increase of 0.76 %, fuel consumption is down by 3.58 %. When the extra goods transported are accounted for, the efficiency gain (amount of fuel per tonne-km) is 4.31 %. Fuel consumption per vehicle-km does increase by 9.34 % on the total.
When LHVs of 25.25 m and 60 t operate in Europe under the same terms as classic heavy goods vehicles (outside of urban areas), they show themselves to be 12.45 % more efficient in terms of fuel consumption per tonne-km performed.
Table 53: Scenario 2 transport energy Consumption
Country Truck type Fuel consumption (tonne) Fuel consumption (million litre) CO2 (tonne)
AT HDT4 403 645 483 1 266 472
Country Truck type Fuel consumption (tonne) Fuel consumption (million litre) CO2 (tonne)
ES HDT6 2 602 013 3 116 8 164 056
FI HDT6 272 495 330 854 979
FR HDT6 1 939 781 2 323 6 086 241
GR HDT6 260 181 312 816 341
HU HDT6 149 470 179 468 975
IE HDT6 3 405 4 10 683
IT HDT6 1 150 437 1 378 3 609 602
LT HDT6 76 472 92 239 939
LU HDT6 10 544 13 33 084
LV HDT6 52 411 63 164 444
NL HDT6 258 740 310 811 820
PL HDT6 827 981 992 2 597 866
PT HDT6 86 034 103 269 938
RO HDT6 414 585 497 1 300 797
SE HDT6 394 896 473 1 239 022
SI HDT6 41 670 50 130 745
SK HDT6 93 493 112 293 342
UK HDT6 348 636 418 1 093 879
TOTAL 32 786 184 39 270 102 869 662
Well-to-tank emissions show the same pattern, as they are 100 % correlated to fuel consumption. The amount of CO2 emitted during fuel production is down 3.58 % to 19 999 572 tonnes.
NOx transport emissions will decrease somewhat more than CO2 emissions, by 4.03 % to 463 593 tonnes for all countries. For PM, the effect is even greater, as a drop of 8.39 % can be expected, mainly due to less non-exhaust PM: fewer kilometres driven cause less resuspension and mechanical wear.
As they are linked directly to fuel consumption, well-to-tank emissions of NOx and PM are down by 3.58 % in comparison to the “business as usual” scenario.
Tables for NOx and PM are added in the annex to this report
3.3. “Corridor/coalition” scenario
In the corridor/coalition scenario, only a select number of countries are assumed to allow LHV on their roads.
Demand will not be stimulated to the same extent as in the previous scenario, yet the fact that a number of industrial centres and distribution hubs are located within the corridor/coalition scenario, combined with national demand growth, still make for significant increases in road volumes in these countries.
The resulting effect on energy consumption is moderate in comparison to the reference scenario. Fuel consumption decreases by 0.58 %, while tonne-km go up by 0.18 %. The average net efficiency gain per tonne-km is 0.76 %.
Compared to the full option scenario, LHVs have a slightly smaller cost advantage to classic HGVs (heavy goods vehicles), at 11.14 %. Main reason for the difference is a marginally lower average load factor in the corridor/coalition countries.
Table 54: Scenario 3 transport energy consumption
Country Truck type Fuel consumption (tonne) Fuel consumption (million litre) CO2 (tonne)
AT HDT4 488 926 586 1 534 049
Total well-to-tank emissions in this scenario amount to 20 621 510 tonnes.
Well-to-tank NOx and PM emissions decrease by the same level as CO2 emissions. NOx emissions will again decrease somewhat more than CO2, by 0.68 %. Around 479 796 tonnes of NOx would be emitted as a consequence of freight transport with heavy trucks. PM emissions go down by 1.27 %.
Tables for NOx and PM are added in the annex to this report.
Tables for NOx and PM are added in the annex to this report.